Measuring arterial and tissue responses to functional challenges using arterial spin labeling

NeuroImage 49 (2010) 478–487

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NeuroImage j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y n i m g

Measuring arterial and tissue responses to functional challenges using arterial spin labeling Yi-Ching Lynn Ho a,b,⁎, Esben Thade Petersen a,b, Xavier Golay a,c,1 a b c

Department of Neuroradiology, National Neuroscience Institute, Singapore Centre for Functionally Integrative Neuroscience, University of Aarhus, Aarhus, Denmark Laboratory of Molecular Imaging, Singapore Bioimaging Consortium, Agency for Science, Technology and Research (A⁎STAR), Singapore

a r t i c l e

i n f o

Article history: Received 19 December 2008 Revised 17 July 2009 Accepted 17 July 2009 Available online 25 July 2009

a b s t r a c t The measurement of cerebral blood flow (CBF) in functional MRI studies that aim for non-invasive, quantitative and reliable measurements is a challenge. Here, we tested the feasibility of a recently developed, model-free CBF technique to study vascular dynamics upon functional challenges. Multiple inversion timepoint signals were measured from arterial and tissue compartments, allowing for the calculation of CBF through a process of deconvolution. Using graded visual stimulation known to produce increasing hemodynamic responses, we recorded significant and graded ΔCBF and Δτm (microvascular arrival time change) that were highly comparable to those estimated by a standard 3-parameter fit based on the general kinetic model, though the absolute values had weaker agreement. Estimated arterial blood volumes (excluding substantial arteriolar contribution) did not show significant change with visual stimulation. Bolus arrival times in the microvascular compartment shortened more as compared to the arrival times from the arterial compartment during visual stimulation, suggesting larger involvement of the microvasculature in local neuronal response. While there are limitations, the model-free analysis method has the potential to offer useful vascular information in fMRI studies. © 2009 Elsevier Inc. All rights reserved.

Introduction The measurement of cerebral blood flow (CBF) in functional MRI studies is gaining momentum, due to advances and accessibility of non-invasive, arterial spin labeling (ASL) techniques (for a review, see Petersen et al., 2006b). There is furthermore, recognition of the need for quantitative fMRI, since it is known that the amplitude of the common BOLD-fMRI signal depends on underlying vascular contributions like CBF and CBV (cerebral blood volume) (Ogawa et al., 1993). Nonetheless, it remains a challenge to get robust estimates of CBF using ASL during brain activation. CBF is typically quantified based on tracer kinetics theory with a single compartment, originally proposed by Kety and Schmidt (1948) and later extended by Buxton et al. (1998) as a general kinetic model for ASL data. The general kinetic model takes into account the magnetization differences between the control and labeled experiments in ASL, for which the travelling time of the magnetized blood is an important factor. Among some of the complexities with ASL techniques, the time taken by the leading and trailing edges of the labeled arterial blood to arrive at the capillary exchange site has been shown to reduce with positive functional activity, as pointed out by ⁎ Corresponding author. Department of Neuroradiology, National Neuroscience Institute, 11 Jalan Tan Tock Seng, 308433, Singapore. Fax: +65 6358 1259. E-mail address: [email protected] (Y.-C.L. Ho). 1 UCL Institute of Neurology, Queen Square, London, UK. 1053-8119/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2009.07.040

several studies using motor and visual paradigms (Buxton et al., 1998; Gonzalez-At et al., 2000; Hendrikse et al., 2003; Yang et al., 2000). These studies pointed out the error of assuming unchanging arrival times if a single inversion time-point acquisition was used, as had been commonly done, until methods such as Q2-TIPS (Luh et al., 1999) and QUIPSS II (Wong et al., 1998) were developed to improve this issue. Inter- and intra-subject variations in arterial, arteriolar and capillary (further defined in the text as “pre-venous”) vascular responses may further compound the problem. The issue of dynamic arrival times can be circumvented through the use of multiple post-labeling delay times (TI) in a single ASL scan with repeated, small flip-angle acquisitions (Gunther et al., 2001), a method which Hendrikse et al. (2003) modified to study perfusion in human visual stimulation. In that latter study, the authors separated arterial hemodynamics from the tissue perfusion by maximum cross correlation of the visual activation with postlabeling (TI) delay. Maximal activation at short TIs (200–400 ms) characterized the voxels with large arterial input, while maximal activation at longer TIs (600–1000 ms) characterized voxels with relatively more tissue contribution. While compelling, this separation is arbitrary and depends on several factors, such as the distance of each imaged voxel from the labeling slab and more importantly, the fraction and composition of the vascular system as covered by individual voxels. A simple solution proposed by Ye et al. (1997) is to use bipolar crusher gradients to exclude the inflowing arterial signal, particularly

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useful at short TIs. These crusher gradients dephase and destroy signal from spins flowing beyond a certain speed. This would help to target flow signals from the microvascular compartment, i.e. smaller arterioles and capillaries, which are known to have a relatively low blood velocity in both animals and humans. On the other hand, the often disregarded arterial signal also contains useful information about the arterial input function (AIF). Using tracer kinetics to quantify flow, a constant input function (e.g. non-dispersed, square function or uniform plug flow) is normally assumed. Such an assumption may not be valid in all conditions. It has been shown that the AIF, as measured by bolus tracking, varies between people and brain regions (Calamante, 2005; Perthen et al., 2002), and predicts ASL measurements (Gall et al., 2008). The AIF might also vary between rest and activity. Identifying the local AIF could potentially help to increase the accuracy of the CBF estimation in functional studies. In this work, we describe the adaptation of a recently developed model-free ASL approach based on multiple inversion time-points and a deconvolution technique (Petersen et al., 2006a) for functional CBF measurements. CBF and microvascular arrival time estimates based on the model-free approach are compared to the typical 3-parameter fit of the general kinetic model (Buxton et al., 1998; Gunther et al., 2001). We also derive novel information regarding macro- and microvascular hemodynamics, including arterial blood volume and arterial arrival times with fine temporal resolution.

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Fig. 1. Simulated ΔM(t) curves across multiple inversion time-points within 1 TR. The green line represents the curve from the non-crushed dataset (Ncr), the blue represents the curve from the crushed dataset (Cr) and the red shows the fractional arterial input function (fAIF), obtained primarily by subtraction of the crushed from the non-crushed ΔM(t) curves. The start of the incline of the fAIF indicates the arrival of the labeled spins on the arterial side (τa), whereas the start of the incline of the Cr curve shows the microvascular arrival time (τm).

Methods Overview of theory: model-free CBF quantification In an ASL experiment, the magnetization difference between the control and labeled measurements gives us a tissue perfusion-weighted signal (a.k.a. the ‘tissue signal’). The general kinetic model (Buxton et al., 1998) describes it as: Z ΔM ðt Þ = 2 · Ma;0 · f ·

t 0

Ca ðt Þ · r ðt − τÞ · mðt − τÞdτ

ð1Þ

where ΔM(t) is the tissue signal, Ma,0 is the equilibrium magnetization for arterial blood, f is the CBF or perfusion rate, Ca(t) is the fractional arterial input function, r(t − τ) is the residue function describing the fraction of labeled spins that arrived at the voxel at time τ and which still remain at time t, and finally m(t − τ) describes the longitudinal magnetization relaxation for the fraction of labeled spins that arrived at time τ and would still be in the voxel at time t. The arterial signal here is represented by the factor 2 U Ma,0 multiplied by Ca(t), while the tissue response is represented by r(t − τ) scaled by the perfusion rate, f. Measuring the tissue and arterial information would allow us to deconvolve the tissue signal by the arterial input function to find the residue function scaled by the flow. The following section is a summary of the theory and method as originally set out in Petersen et al. (2006a), with formulas provided with errata corrected where necessary. The arterial and tissue information needed to find the flow could be provided by the acquisition of a pair of ASL scans with Look–Locker, multiple inversion time-point readout — one with bipolar crushers and one without (Petersen et al., 2006a). The crushed scan would yield the tissue signal, given that fast-flowing arterial spins are excluded. Ca(t), the fractional arterial input function that also describes the inflowing magnetization, could be estimated from the subtraction of the crushed scan (ΔMcr(t)) from the non-crushed scan (ΔMncr(t)), since the latter would hold combined information about the arterial as well as tissue signals (Fig. 1). While ΔMncr(t) − ΔMcr(t) provides the shape of the AIF, scaling by the arterial blood volume fraction (aBV) should be done, since with typical ASL resolution there will be no voxel fully filled with blood from which to select a pure AIF. The aBV can be estimated from the area under ΔMncr (t) − ΔMcr(t),and should be adjusted for the T1 relaxation of labeled arterial blood and compared as a ratio to the value in a blood-filled voxel, estimated as 2 U Ma,0 τb, where τb is the bolus duration. This area has to take into account the inversion efficiency, α, as well as the multiple saturation pulses due to the Look–Locker readout (Gunther et al., 2001) with the term, cosnφ, where φ is the flip angle and n = floor((τm − τa) / ΔTI), with ΔTI being the time between inversions. τm is the microvascular arrival time, while τa is the arterial arrival time, as illustrated in Fig. 1. The latter correction is especially necessary if the ΔTI is small, i.e. b200 ms, since the time taken by a bolus to travel from the arterial to the microvascular compartment in a voxel could be 200 to 300 ms, meaning that the bolus would experience multiple excitation pulses during such a transit. The aBV can be expressed as:2 Z aBV =

∞

−∞



t = T1;a

ðΔMncr ðt Þ − ΔMcr ðt ÞÞe

2 · Ma;0 · τ b · α · cosn u

 dt ð2Þ

2 Erratum in the original equation [Eq. 10], Petersen et al (2006a). There is also the addition of the cosnφ factor in the denominator here to apply to cases when renewal of arterial blood between inversions cannot be assumed.

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where T1,a is the longitudinal relaxation time of arterial blood. Overall, after correcting for aBV, as well as factoring in the magnetization decay, the AIF can be expressed as: 0 1 B n AIFðt Þ = 2 · Ma;0 · τ b · α · cos u · B @Z

∞ −∞

ðΔMncr ðt Þ − ΔMcr ðt ÞÞet = T1;a t V= T1;a

ðΔMncr ðt VÞ − ΔMcr ðt VÞÞe

dt

0

C C · e − ðt A

+ ðτ m − τa ÞÞ = T1;a

:

ð3Þ

Functional magnetic resonance imaging experiments All experiments were performed on a clinical 3.0 T scanner (Philips Healthcare, Best, The Netherlands) using standard setup of body coil transmission and an 8-channel SENSE head coil for reception. Perfusion-weighted scans were done using the ASL QUASAR (quantitative STAR labeling of arterial regions) sequence (Petersen et al., 2006a), which combines a PULSAR labeling technique (pulsed STAR labeling of arterial regions, described in Golay et al. (2005) as a modified EPISTAR sequence) with a GRE-EPI, Look–Locker readout strategy (Gowland and Mansfield, 1993) for sampling at multiple inversion time-points and a repetitive Q2-TIPS-like bolus saturation scheme for clear definition of the arterial blood bolus (Luh et al., 1999; Wong et al., 1998). The parameters were: TR/TE = 3000/23 ms, flip angle = 25°, ΔTI = 100 ms, inversion time-points = 26, τb = 1.05 s, SENSE factor = 2.5, labeling slab = 150 mm, inversion gap = 30 mm, FOV = 224 × 224 mm, Matrix = 64 × 64, slice = 1, and slice thickness = 5 mm. Each QUASAR scan was performed twice, once with bipolar or “crusher” gradients (Venc = 3 cm/s in foot–head direction, 120 pairs of control and labeled scans, total scan time = 12 min) and the other without crusher gradients (45 pairs of control and labeled scans, total scan time = 4 min and 30 s). All scans were performed on an oblique slice corresponding to the calcarine sulcus. To locate the primary visual cortex, we targeted the calcarine sulcus using T1-weighted sagittal scans (TR = 3800 ms, TE = 15 ms, flip angle = 90°, Matrix = 512 × 512). Six healthy volunteers (5 males, 1 female; age range 29 to 33 years) gave informed, written consent prior to participation. The protocol was approved by the Institutional Review Board. The functional paradigm consisted of a gray-scale, 8 Hz radial checkerboard pattern with 3 levels of visual contrast (25%, 50%, 100%) that alternated with a baseline condition of an iso-luminance gray (50%) background. The order of the visual contrast blocks was randomized for each subject. To maintain alertness and visual fixation, subjects were required to focus on a small arrow in the middle of the screen that pointed in one of four directions and respond to the directional changes by pressing a corresponding button. The visual stimulation and response tracking were controlled by the Eloquence system (Invivo, Orlando, FL, U.S.A) that was mounted onto the head coil. For the ASL scan with crusher gradients, the run consisted of 10 baseline blocks (15 volumes each) alternating with 9 visual blocks (10 volumes each). For the scan without crushers, the run length only consisted of 4 baseline blocks alternating with 3 visual blocks, as SNR was comparatively greater. In order to minimize movement due to relatively long scan times, each subject had a customized Styrofoam mould that cradled the head, neck and shoulders, essentially locking the upper body. Data analysis Postprocessing was done on a Windows PC using in-house programmes written in IDL 6.1 (ITT Visual Information Solutions, Boulder, CO, U.S.A.). Preparation of data Linear interpolation of both the control and labeled data was done before any subtraction in order to more accurately match the BOLD effects for each point in time (Lu et al., 2005). Perfusion-weighted images (ΔMcr(t) and ΔMncr(t)) were then derived from pair-wise subtraction of the interpolated labeled data from the interpolated control data. The first 3 time volumes (9 s) of each visual block and the first 5 time volumes (15 s) of each baseline block were excluded from the analyses to ensure steady-state measurements. From the remaining steady-state volumes, we derived the curves, ΔMcr(t) and ΔMncr(t) for each visual contrast and baseline condition using the multiple inversion time-points within each volume. ROI detection The peak inversion time-points of the crushed tissue curves (ΔMcr(t)) were averaged for each time volume and voxel. Wilcoxon Rank–Sum tests (p b 0.001, uncorrected for multiple comparisons) were used to determine significant activation during visual stimulation as compared to baseline, using the mean of the peak inversion time-points for each time volume. This was done on a voxel-by-voxel basis with a cluster threshold of 5 voxels. Significantly activated voxels that were common to all three visual contrast conditions in both the crushed and non-crushed scans were then taken as the final region of interest (ROI). Local AIF selection and aBV calculation On a voxel-by-voxel basis, the simple subtraction of ΔMcr(t) from ΔMncr(t) provided AIF and aBV estimates, albeit unscaled. To avoid using illdefined AIFs, voxels with the largest aBV estimates as well as the best fAIF fits to a gamma-variate function were identified from each ROI. The best two fAIFs voxel-wise were selected and they were corrected by the factors as per Eq. (3) and averaged. Calculations of aBV were done according to Eq. (2) and expressed as a voxel fraction. Inversion efficiency, α was 0.95, as measured on a phantom (Petersen and Golay, 2007b). The bolus duration, τb was 1.05 s. Both the AIF and tissue curves (ΔMcr(t)) were fitted with gamma functions for the subsequent deconvolution and arrival time detection. To check the gamma fits, we assessed the Root Mean Square deviations, which were 6.2 ± 1.5 (s.d.) % of the maximum height of each raw curve. The distributions for the raw vs fitted curves were not dissimilar (Kolmogorov–Smirnov Z tests, α = 0.05). The fits were further verified by visual inspection. Arrival time detection Bolus arrival times were estimated using an edge detection routine, as described in Petersen et al. (2006a). τm, the microvascular arrival time was provided by the rising edge of the gamma-fitted, crushed tissue curve, while the rising edge of the gamma-fitted AIF provided the arterial

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arrival time (here denoted as τa,i). Other than the sites of AIF sampling, the remaining voxels within the ROI would also contain an arterial contribution, but smaller in ratio compared with gray matter volume, and perhaps further down the arterial tree. Another ‘arterial’ arrival time (denoted as τa,ii) was also extracted from these voxels by similarly subtracting ΔMcr(t) from ΔMncr(t) and detecting the rising edge of the resulting curve. Ma,0 estimation This was estimated for each individual subject by fitting the saturation recovery curve of gray matter (using the ROI) in the non-crushed scan, taking into account slice profile effects and assuming the brain–blood partition coefficient for water, λ = 0.98 (Petersen and Golay, 2007a). Deconvolution CBF changes for each condition were computed by a deconvolution of Eq. (1), based on the method of truncated singular value decomposition (SVD), which Ostergaard et al. (1996) found to be stable even at low SNR. This method of regularization neglects the singular values below a certain threshold by lowering their rank and consequently stabilizes the system. 3-parameter fit To test the deconvolution against the standard fit of the general kinetic model, we fed the tissue curve to the modified 3-parameter fit of the model proposed by Gunther et al. (2001) to accommodate multiple low flip-angle readouts. It can be expressed as:

ΔMcr ðt Þ =

8 > > > > > <

0;

tbτm



 δR · ðt − τm Þ ; · 1 −e

−2 · Ma;0 · f −R ·t · e 1;a δR > > >   > > : −2 · Ma;0 · f · e − R1;a · ðτm + τb Þ · 1 −eδR · ðτb Þ · e − R1;app;eff ðt − ðτm δR

τm V t b τm + τb + τ b ÞÞ

;

ð4Þ

tzτm + τb

where ΔR = R1,a − R1,app,eff and R1,app,eff = R1 + f / λ − ln(cosφ) / ΔTI, where φ is the flip angle and ΔTI is the interval between the excitation pulses. The acquired signal was then corrected for multiple readouts in the same way as what was done for the model-free approach. From the above equation, the three parameters that could be allowed to vary would be τm, τb and CBF. In our case, τm and CBF were fitted while τb was fixed at 1.05 s, as defined by the Q2-TIPS-like bolus saturation. Statistical tests Comparisons between CBF and τm estimates derived by the model-free and the 3-parameter fit techniques were made using paired t-tests and Pearson correlations. For all parameter changes between baseline and visual stimulation, paired t-tests were used. Linear regressions were performed to assess the relative magnitude of arrival time changes. Statistical significance was set at p ≤ 0.05, two-tailed. SNR calculations To assess the quality of the data, two types of SNR were calculated. Firstly, Ma,0 was compared to the standard deviation of the non-crushed, unsubtracted data points during baseline − SNRM0. The last inversion time-point of each TR was selected on a voxel-by-voxel basis, as it demonstrated the largest deviations. Secondly, the temporal SNR − SNRt of the crushed, subtracted data (ΔMcr) was also assessed. The mean of the baseline time volumes averaged from the peak inversion time-points was compared against its standard deviation. Results Cerebral blood flow (CBF) Using both the model-free approach and the 3-parameter fit, significant CBF changes in the primary visual cortex were detected in all subjects and their amplitudes corresponded to the graded visual paradigm used. Table 1 reports the absolute and ΔCBF [%] values per condition for both methods of estimation. For each method alone, CBF for each visual contrast condition was significantly greater than baseline (p b 0.05), and that in addition, the flow for the 100% visual contrast was significantly greater than both the 25% and 50% visual contrast conditions (p b 0.05). The flow values for the 25% and 50%

visual contrasts were statistically undifferentiated from each other regardless of either method of estimation. Fig. 2 shows the CBF trends relating to the visual paradigm as estimated by the model-free method (in solid circles) and the 3parameter fit (in solid squares) for each of the six subjects. Both methods yielded CBF estimates that were highly correlated with each other (absolute values: r = 0.88, p b 0.001; ΔCBF [%] from baseline: r = 0.75, p b 0.001), though, the model-free flow values were on average 26 ml/100 g/min lower than that of the 3-parameter fit (Confidence Interval (CI): [−22, −30 ml/100 g/min]). Looking at ΔCBF [%] from baseline flow, a comparison between the two methods yields a mean difference of only 1.5 ± 4.5 (SEM) % points for all stimulation conditions. A paired t-test could not detect any

Table 1 CBF and τm in terms of mean ± SEM, plus the average individual change from baseline in brackets (% mean ± SEM), as estimated using the model-free approach and the 3-parameter fit of the general kinetic model. τm [s]

CBF [ml/100 g/min] Baseline 25% visual contrast 50% visual contrast 100% visual contrast

Deconvolution (model-free approach)

3-parameter fit

Edge detection (model-free approach)

3-Parameter fit

50 ± 6 69 ± 7 (43 ± 11%) 69 ± 10 (41 ± 13%) 80 ± 8 (66 ± 10%)

67 ± 5 95 ± 6 (42 ± 7%) 98 ± 8 (46 ± 7%) 111 ± 7 (66 ± 4%)

0.68 ± 0.07 0.57 ± 0.05 (− 15 ± 4%) 0.51 ± 0.06 (− 25 ± 4%) 0.53 ± 0.05 (− 22 ± 3%)

0.88 ± 0.07 0.74 ± 0.06 (− 16 ± 2%) 0.70 ± 0.06 (− 20 ± 2%) 0.68 ± 0.06 (− 23 ± 2%)

The third parameter — the bolus duration was fixed at 1.05 s by a Q2-TIPS-like bolus saturation.

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Fig. 2. CBF and τm estimates from the model-free approach (circles) vs the 3-parameter fit (squares) by visual contrast (VC) for all 6 subjects. Model-free CBF values (solid circles) are lower than that from the 3-parameter fit (solid squares), while the edge-detected τm values (open circles) are shorter than the fitted timings (open squares). However, the trends according to visual contrast levels are similar for both methods and ΔCBF [%] and Δτm [%] values were undifferentiated between both methods.

significant difference between the ΔCBF [%] calculated from both methods (CI: [− 8%, 11%]; p = 0.75). Figs. 3a, b show the ΔM curves for two subjects by visual contrast condition. They demonstrate the detectable perfusion-weighted signals in the functional ASL scans, and are comparable to the simulated fractional AIF and tissue curves in Fig. 1. Figs. 3c, d show the tissue curves from Figs. 3a, b magnified respectively and with error bars in both directions representing 1.96 SEM each (SEM = std.dev. / sqrt(no. of voxels in ROI)), which provides a rough estimate of the confidence intervals for the values at each inversion time-point from a spatial perspective, assuming normality. The ROI for the first subject contained 48 voxels, while the ROI for the second subject contained 55 voxels. The error bars show a reasonable spread of values among the ROI voxels. The inset plots illustrate the position of the edge-detected arrival times on the respective curves, further magnified. Figs. 4a, b show the statistically thresholded ROIs and the sampling sites of the AIFs used in the deconvolution for the two subjects whose data were shown in Fig. 3. Figs. 4c, d illustrate the voxels with significantly shortened arterial and microvascular arrival times during visual stimulation, as overlaid onto crushed ASL images for the same subjects. Bolus arrival times Table 1 reports the average microvascular arrival time, τm as estimated by both edge detection and the 3-parameter fit of the tissue curve (ΔMcr(t)) for the different experimental conditions. The edgedetected τm was shorter by 0.17 s on average, as compared to the fitted timing (CI: [− 0.20, −0.15 s]; p b 0.001). Nonetheless, both estimations of τm were found to be highly correlated with each other (r = 0.91, p b 0.001). This can be seen in Fig. 2, where the τm from both methods (edge detection — open circles; 3-parameter fit — open squares) appear to have the parallel trends of shortening with visual stimulation in every subject. Similar to what was seen for ΔCBF, both methods gave relative timing changes from baseline (Δτm) that were not unlike each other, with the mean difference being 1.5 ± 1.5 (SEM) % points (CI: [−4.6%, 1.6%]; p = 0.34).

In addition to τm, two other arrival times were derived using edge detection: τa,i (the arterial arrival time at the sites of AIF sampling for the deconvolution) and τa,ii (the arterial arrival time in the rest of the ROI, excluding the voxels for AIF sampling) Fig. 5a shows the all three edge-detected arrival times across the visual conditions. Over all conditions, τa,i was the shortest, followed closely by τa,ii and then τm, all of which were significantly different from each other (p b 0.001). Visual stimulation caused all these three timings to shorten from baseline (p b 0.02). Moreover, τm continued shortening between the 25% and 50% visual contrast conditions (p b 0.02), while τa,i and τa,ii remained similar for all three visual contrast conditions. The disparity in shortenings of the arrival times across conditions can be seen more clearly in Fig. 5b, where the differences [τm − τa,i] and [τa,ii − τa,i] are calculated across conditions per subject and plotted. Linear regressions showed that [τm − τa,i] varied significantly with visual stimulation (p ≤ 0.05), while [τa,ii − τa,i] did not. Arterial blood volume (aBV) Table 2 reports the blood volumes (aBVi and aBVii) estimated for two sets of data — (i) at the sites of AIF sampling (the top two relatively blood-filled voxels within the ROI), and (ii) the rest of the ROI. There were no statistically significant differences for both aBVi and aBVii when comparing baseline and visual stimulation, though there seemed to be a slight trend for aBVii, to increase with stimulation within each subject. Discussion Comparisons between the 3-parameter fit and the model-free approach The model-free approach and the 3-parameter fit showed weak agreement for absolute CBF and absolute microvascular arrival time (τm) estimation, but when relative changes or trends from baseline were compared, the differences resolved. With regard to absolute CBF values, the model-free values were consistently lower than the fitted values by an average of 26 ml/100 g/min. The tendency for the

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Fig. 3. (a, b) ΔM(t) curves from two subjects: fractional AIFs (broken lines) used in the deconvolution with tissue curves (solid lines) by each condition (gray — baseline, green — 25% VC, blue — 50% VC, red — 100% VC). (c, d) Tissue curves from (a, b) magnified respectively with their gamma fits in dotted lines. Error bar in each direction = 1.96 ⁎ SEM, where SEM = std.dev./sqrt(no. of voxels in ROI). The inset plots illustrate the location of the detected arrival times on the further magnified curves.

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Fig. 4. (a, b) Statistically thresholded ROIs (red) and within them the AIF sampling sites (bright orange) from the same two subject datasets in Fig. 3 overlaid onto T1-weighted images. (c, d) For the same subjects, voxels with significantly shortened arterial and microvascular arrival times during visual stimulation, as overlaid onto crushed ASL images (τa,i and τa,ii in red, τm in blue and their intersection in purple).

model-free approach to yield lower CBF values was also noted in the study by Petersen et al. (2006a), where simulations showed that the SVD algorithm used in the model-free approach tended to underestimate the true flow by ∼17%, while the 3-parameter fit yielded values more in line with the true flow, though overestimating it by ∼ 7%. In that work, circular SVD was used for the model-free approach, while standard truncated SVD was employed in this study. Nonetheless, as also seen in other applications like DSC-MRI (Wu et al., 2003), underestimation of the flow by either form of SVD is expected, because regularization is known to underestimate the flow. On the other hand, the flow overestimation by the 3-parameter fit might also come from the estimated non-dispersed arterial input function, clearly different from the measured AIFs in our study (Figs. 3a, b). Another factor is the discrepancy in the microvascular arrival time (τm) estimated by both the 3-parameter fit and the edge detection of the tissue curve. The longer τm estimates from the 3-parameter fit could contribute to higher fitted flow values, all else being equal. Conversely, the edge-detected τm could also be earlier than the true timing due to the tissue curve being subject to flow dispersion, voxel averaging and errors from the gamma fit prior to edge detection, bearing in mind though, the relatively good gamma fit as indicated by small RMS deviations and insignificant Kolmogorov–Smirnov Z test results reported earlier in the Methods. Nevertheless, a τm detected too early could potentially contribute to the ultimate underestimation of the flow value via deconvolution. Importantly however, when we consider the relative Δτm [%] and ΔCBF [%], both methods of estimation produced remarkably similar

changes across the graded visual stimulation from baseline. The mean differences between both methods of estimation for Δτm and ΔCBF were 1.5 ± 1.5% points and 1.5 ± 4.5% points respectively, both of which were statistically insignificant. This good agreement for relative arrival time and flow changes is reassuring for functional studies using either of these methods on multiple inversion time-point data to estimate Δτm and ΔCBF, given that the change ratios appear similar. The individual ΔCBF [%] that ranged from 10–90% across the different visual contrasts were within the typical range of values reported in the vast MR literature on flashing checkerboard stimulation (e.g. Hoge et al., 1999; Leontiev and Buxton, 2007). Bolus arrival times reflecting vascular response With the good agreement from the 3-parameter-fitted Δτm in mind, we discuss the three different arrival times (arterial — τa,i, τa,ii and microvascular — τm) estimated from edge detection and postulate that they are likely to reflect several pre-venous, vascular segments, which are known to be differentiated by speed of flow, even though there will be no clear anatomical or functional boundary in reality. Taken from the entry of un-dephased fast-flowing spins, τa,i is likely to indicate the bolus arrival at the arterial segment within the two voxels with the largest aBVs. Excluding these two voxels, τa,ii possibly reflects the bolus arrival at the arterial segment in the rest of the ROI voxels. τm should represent the bolus arrival at the microvascular segment (smaller arterioles and capillaries), since any labeled blood signal flowing faster than 3 cm/s would be eliminated by the diffusion

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Fig. 5. (a) Arrival times (plus SEMs) for baseline and all visual contrasts (VC). τa,i is the arterial arrival time at the AIF sampling sites, τa,ii is the arterial arrival time for the rest of the ROI, and τm is the microvascular arrival time for the same area as the latter. (b) Timing differences across the 4 conditions: each point represents the timing difference for one subject. Some points overlap. The difference between τm and τa,i tends to get smaller with increasing stimulation, given a significantly negative slope (p ≤ 0.05). The other slope was not significant. Overall, τm shortens more than the τa,i and τa,ii with increasing neuronal activation.

gradients (Ye et al., 1997). In animal studies using optical techniques, arterioles and capillaries were recorded as having flow velocities of approximately 1–3 cm/s and 0.1 cm/s or lower respectively (Hutchinson et al., 2006; Kleinfeld et al., 1998; Kobari et al., 1984; Villringer et al., 1994; Zweifach and Lipowsky, 1977), while in human retinal arterioles (∼ 110 μm diameter) velocity was measured by laser flowmeter to be ∼3 cm/s (Gilmore et al., 2005). On the other end of the spectrum, arteries (posterior, middle and anterior cerebral) in human brain measured with transcranial Doppler ultrasound had flow velocities of beyond 30 cm/s (Lisak et al., 2005; Sorteberg et al., 1990; Zaletel et al., 2004). Therefore, our choice in the ‘crushed’ scan to dephase spins moving beyond a velocity of 3 cm/s should eliminate large arterial contributions, particularly during the early inversion times (Petersen et al., 2006a), and target specifically the microvascular compartment. On the whole, the absolute timing of each ASL Table 2 Arterial blood volumes (mean ± SEM) from the sites of AIF sampling (aBVi) and the rest of the ROI (aBVii) for each of the four experimental conditions.

Baseline 25% visual contrast 50% visual contrast 100% visual contrast

aBVi [%]

aBVii [%]

4.04 ± 0.8 4.47 ± 1.1 4.35 ± 1.1 4.32 ± 1.1

0.98 ± 0.2 1.02 ± 0.3 1.11 ± 0.3 1.14 ± 0.2

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bolus arrival is a function of vascular anatomy and labeling duration, as well as the gap between the labeling and imaging planes, and as such is arbitrary. What is of interest here is the relative difference in arrival times between the baseline and various levels of visual stimulation, which would likely reflect vascular response, since all other factors would be constant. All three arrival times were differentiated from each other, with the microvascular arrival time, τm being the longest, since the bolus takes time to traverse the vascular network. It was notable that on the arterial side, τa,i and τa,ii were significantly different, given that both were detected from the local AIFs within the ROI, with the only difference being that for τa,i, it was from the two voxels with the largest aBVs. This could suggest that the two voxels contained partly large arteries with high inflow, while the rest of the voxels within the ROI likely included arteries of smaller diameter with slightly later arrival or slower flow. Moreover, with visual stimulation all three timings shortened, indirectly indicating higher blood flow velocity in both macro and microvessels upon brain activation. The Δτm findings were further reinforced by the fact that the estimations from the 3-parameter fit showed the same phenomenon. These findings are in line with reports of flow velocity increases during stimulation. In large vessels, as observed in human transcranial Doppler ultrasound studies in P1 and P2 segments of the posterior cerebral artery, in which flow velocity during visual stimulation was reported to increase between 10–50% (Panczel et al., 1999; Sturzenegger et al., 1996). In small vessels, such as rat capillaries, 2-photon laser scanning microscopy has shown up to 50% increase in red blood cell velocity during sensory stimulation (Kleinfeld et al., 1998). Given that all three arrival times were found to be consistently shortened upon stimulation, it could be possible to use these parameters as a substitute for the standard perfusion-weighted signal change to find voxels that might be considered significantly ‘activated’, as demonstrated in Figs. 4c, d. Of the three arrival times, Δτm was the only one that was notably differentiated between the visual contrast conditions. Although the Δτm response seemed to plateau after the 50% visual contrast condition (also shown by the Δτm of the 3-parameter fit), it is remarkable that gradations in visual contrast can elicit differential microvascular responses, while the arterial side appeared less responsive. This could indirectly reflect the role of the microvasculature in regulating local blood flow to serve neuronal or metabolic demands. It is thought that CBF varies acutely according to the demands of the tissue at a very local vascular level (i.e. arteriolar and smaller), and this is associated with modulating vessel diameters to control blood flow to the tissue (Kuschinski, 1996). Such ability of the small vessels to dilate or constrict has recently been shown to be controlled through smooth muscle banding and vascular pericytes (Harrison et al., 2002; Peppiatt et al., 2006). Our results add support to the idea of an important role for the microvasculature in the regulation of neurovascular coupling. Arterial blood volume (aBV) Taken from the top two relatively blood-filled voxels within the ROI (to provide well-defined AIFs for the deconvolution), the aBVi values (as a fraction of a voxel) were expectedly higher than that for the rest of the ROI (aBVii) by about four times, indicating possible presence of sizeable arteries. The values for aBVii on the other hand, should closely reflect average gray matter arterial blood volumes. Taking the baseline aBVii of 0.98% and an average arterial to total cerebral blood volume (CBV) ratio of 0.24 (An and Lin, 2002; Ito et al., 2001 for occipital cortex), the gray matter, total resting CBV would be approximately 4.1%, within the range of reported PET measurements (Greenberg et al., 1978; Ito et al., 2005). Both aBVi and aBVii did not register substantial increases with visual stimulation, perhaps indicating insignificant large vessel

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reactivity during cortical activity. Nonetheless, as can be seen from Table 2, there was a slight tendency for aBVi,ii to enlarge with stimulation within each subject. A couple of recent studies have indeed reported increases in aBV during neuronal activity (Brookes et al., 2008; Kim et al., 2007). One difference in those studies, though, was that their targeted arterial compartment included arterioles, whilst in ours a certain proportion of the arterioles were excluded due to the gradient crushing at 3 cm/s in the foot–head direction. If the vascular resistance response to neuronal demands is local, with a sizeable contribution from the microvessels like the arterioles and capillaries, then it may partly explain both our aBV values, since they reflect mainly large vessels. Still, there is a difficulty that could have affected our measurement of aBV during stimulation — any measured increase in aBV during stimulation could have been due to an artificial inclusion of a larger segment of vasculature considered ‘arterial’. In the scan with velocity encoding, water spins faster than 3 cm/s were dephased during baseline and stimulation. The situation becomes difficult to untangle in the case of a voxel with ‘micro’ vasculature in which blood velocity is below 3 cm/s at baseline, but increases and crosses the 3 cm/s threshold during stimulation, for it would lead to the artificial enlargement of the complementary compartment, i.e. the ‘arterial’ signal upon stimulation. It would certainly not be an issue for capillary signals, as their flow velocity is far less than even 1 cm/s, so even with the large velocity increases of more than 100% found in hypercapnia (Hutchinson et al., 2006; Villringer et al., 1994), the 3 cm/s threshold would not be reached. It may however pose a problem for the arteriolar signal, as its baseline flow velocity could be close to the 3 cm/s threshold (Gilmore et al., 2005; Kobari et al., 1984) and so a maximum 50% velocity increase during sensory stimulation as seen in arteries (Panczel et al., 1999) and capillaries (Kleinfeld et al., 1998) could be enough to tip it into the ‘arterial’ compartment. This issue of changing velocities is also pertinent to ASL techniques that use velocity selective encoding to perform fMRI (Wu and Wong, 2007). Overall, the potential artifact of a fluctuating ‘arterial’ compartment would be a function of the strength and direction of the crusher gradients used, together with the vascular structure under study. Other technical and theoretical considerations The selection of well-defined local AIFs to use in the deconvolution is important. In this study, the cortical area under observation was the primary visual cortex, which is known to be supplied by the calcarine branches of the posterior cerebral artery. The oblique ASL slice acquired at the level of the calcarine sulcus would certainly include parts of the calcarine arterial branches, which should provide strong AIFs. The heterogeneity of vascular anatomy and the resulting fAIFs are partly illustrated in Figs. 3a,b and 4a,b. The different timings and shapes of the AIFs could reflect individual differences in arterial vasculature like length, diameter and orientation of the vessels, with the latter influencing the signal that is being targeted by the crushing scheme. It should also be pointed out that errors in the subtraction of the crushed from the non-crushed data could result in artifactual AIF amplitudes if there are aBV differences in the crushed and noncrushed scans, due for example to motion. Based on simulated curves (Fig. 1), checks of all fAIF curves showed the expected temporal behavior (i.e. a rise and fall to baseline, which would not happen if the crushed and non-crushed ΔM curves were not matched), suggesting that such subtraction errors were unlikely to be large in this study. While the model-free technique used here has the potential to offer a lot of information, it relies on the acquisition of two ASL datasets — one with the arterial signal crushed and the other without any crushing. This doubles the amount of time needed for an experiment. It also becomes imperative that head positioning is consistent and both scans are coregistered well to each other.

Alternatively, both acquisitions, with or without bipolar crushing gradients could be present in the same scan to avoid head repositioning between different scans. However, this would result in relatively long scan times, which might be counterproductive. Finally, a simple measurement using only the crushed datasets could be performed together with a 3-parameter fit to the general kinetic model to get a robust estimate of CBF, at the cost, however, of omitting the information on arterial aspects, such as the arterial arrival time and the arterial blood volume. The crusher scheme used in this study was restricted to the foot– head direction, thus limiting the interpretation of the estimates of CBF, aBV and the arrival times as being purely arterial or tissue, since arterial spins in the other directions would not have been crushed. Nonetheless, the measured parameters would have been weighted towards either the arterial or tissue compartment. Crushing in the foot–head direction was chosen assuming main penetrating arterial supply in this direction. Crushing additionally in all other directions could be implemented, but is in practice not feasible, as the penalty in the SNR would be too large in a sequence (ASL) already known to be poor in SNR. As compared to single inversion time-point ASL methods, two trade-offs with the acquisition of multiple inversion time-points are SNR and slice coverage. In this study, the average SNRM0 was 199, which is decent in the light of the expected 0.5% perfusion-weighted signal from the tissue curves. The average SNRt of the ΔMcr baseline data was 2.3. Temporal and spatial averaging of the data is thus useful to increase the statistical power of the observations. Furthermore, having multiple inversion time-points will require short ΔTIs and this limits the number of slices that can be acquired within each ΔTI. For example, the ΔTI of 100 ms used in this experiment would allow a maximum of three slices. This makes it unsuitable right now for fMRI studies focusing on the whole brain. While this study assessed CBF levels in steady-state conditions, one could imagine further studies aimed at characterizing the temporal dynamics of the CBF response time courses. It has to be noted, however, that ASL techniques and the CBF kinetic model for quantification rely on the assumption of a steady-state, i.e. a stationary, linear and time-invariant system, whereas the aim of functional studies is to vary flow responses (Petersen et al., 2006b). Thus, the difficulty comes from the fact that hemodynamic responses tend to be on the order of 3–7 s and this is within the duration required for a typical control and labeled ASL image pair to be acquired, given standard repetition times (TR) of 2–3 s. This makes it difficult if not impossible to compare in a precise manner the temporal dynamics of ASL-derived CBF time courses with other hemodynamic parameters like cerebral blood volume (CBV) and the blood oxygen level dependent effect (BOLD). Applications Temporal and spatial differences in hemodynamic responses have been shown to account in part for the often perplexing fMRI results in diverse physiological conditions, such as respiratory challenges and aging (Cohen et al., 2004; D'Esposito et al., 2003). With the ability to estimate vascular dynamics, our multiple inversion time-point, model-free method may be suited to functional studies with physiological challenges and/or subjects with non-typical neurovascular responses, such as the very young and old. Conclusion We have shown the viability of a recently developed, model-free ASL technique to provide quantitative CBF values in functional challenges. We recorded significant and graded ΔCBF and Δτm with graded visual stimulation that were highly comparable to those estimated by the standard 3-parameter fit based on the general

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kinetic model, although there was less agreement for absolute values. A greater shortening of τm, as compared to τa,i and τa,ii was observed with visual stimulation, suggesting an important role for the microvasculature in controlling local blood flow in response to neuronal activation. Bearing in mind the technical limitations that could be improved in future, the model-free analysis method offers in addition to flow quantification, the possibility of separating the contributions of local, pre-venous vasculature to hemodynamics. Acknowledgments The authors thank Dr Ivan Zimine for valuable technical assistance, Dr Albert Gjedde for useful discussions and Dr Yih-Yian Sitoh for administrative assistance. This study was supported by a grant from the National Medical Research Council, NMRC/0855/2004. Y-C. L. Ho is supported in part by the Singapore Millennium Foundation. References An, H., Lin, W., 2002. Cerebral venous and arterial blood volumes can be estimated separately in humans using magnetic resonance imaging. Magn. Reson. Med. 48, 583–588. Brookes, M.J., Morris, P.G., Gowland, P.A., Francis, S.T., 2007. Noninvasive measurement of arterial cerebral blood volume using Look–Locker EPI and arterial spin labeling. Magn. Reson. Med. 58, 41–54. Buxton, R.B., Frank, L.R., Wong, E.C., Siewert, B., Warach, S., Edelman, R.R., 1998. A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magn. Reson. Med. 40, 383–396. Calamante, F., 2005. Bolus dispersion issues related to the quantification of perfusion MRI data. J. Magn. Reson. Imaging 22, 718–722. Cohen, E.R., Rostrup, E., Sidaros, K., Lund, T.E., Paulson, O.B., Ugurbil, K., Kim, S.G., 2004. Hypercapnic normalization of BOLD fMRI: comparison across field strengths and pulse sequences. Neuroimage 23, 613–624. D'Esposito, M., Deouell, L.Y., Gazzaley, A., 2003. Alterations in the BOLD fMRI signal with ageing and disease: a challenge for neuroimaging. Nat. Rev. Neurosci. 4, 863–872. Gall, P., Petersen, E.T., Golay, X., Kiselev, V.G., 2008. Delay and dispersion in DSC perfusion derived from a vascular tree model predicts ASL measurements. Proc. 16th ISMRM, Toronto, Canada (Abstract 627). Gilmore, E.D., Hudson, C., Preiss, D., Fisher, J., 2005. Retinal arteriolar diameter, blood velocity, and blood flow response to an isocapnic hyperoxic provocation. Am. J. Physiol. Heart Circ. Physiol. 288, H2912–H2917. Golay, X., Petersen, E.T., Hui, F., 2005. Pulsed star labeling of arterial regions (PULSAR): a robust regional perfusion technique for high field imaging. Magn. Reson. Med. 53, 15–21. Gonzalez-At, J.B., Alsop, D.C., Detre, J.A., 2000. Cerebral perfusion and arterial transit time changes during task activation determined with continuous arterial spin labeling. Magn. Reson. Med. 43, 739–746. Gowland, P., Mansfield, P., 1993. Accurate measurement of T1 in vivo in less than 3 seconds using echo-planar imaging. Magn. Reson. Med. 30, 351–354. Greenberg, J.H., Alavi, A., Reivich, M., Kuhl, D., Uzzell, B., 1978. Local cerebral blood volume response to carbon dioxide in man. Circ. Res. 43, 324–331. Gunther, M., Bock, M., Schad, L.R., 2001. Arterial spin labeling in combination with a Look–Locker sampling strategy: inflow turbo-sampling EPI-FAIR (ITS-FAIR). Magn. Reson. Med. 46, 974–984. Harrison, R.V., Harel, N., Panesar, J., Mount, R.J., 2002. Blood capillary distribution correlates with hemodynamic-based functional imaging in cerebral cortex. Cereb. Cortex 12, 225–233. Hendrikse, J., Lu, H., van der, G.J., van Zijl, P.C., Golay, X., 2003. Measurements of cerebral perfusion and arterial hemodynamics during visual stimulation using TURBO-TILT. Magn. Reson. Med. 50, 429–433. Hoge, R.D., Atkinson, J., Gill, B., Crelier, G.R., Marrett, S., Pike, G.B., 1999. Investigation of BOLD signal dependence on cerebral blood flow and oxygen consumption: the deoxyhemoglobin dilution model. Magn. Reson. Med. 42, 849–863. Hutchinson, E.B., Stefanovic, B., Koretsky, A.P., Silva, A.C., 2006. Spatial flow-volume dissociation of the cerebral microcirculatory response to mild hypercapnia. Neuroimage 32, 520–530. Ito, H., Kanno, I., Fukuda, H., 2005. Human cerebral circulation: positron emission tomography studies. Ann. Nucl. Med. 19, 65–74. Ito, H., Kanno, I., Iida, H., Hatazawa, J., Shimosegawa, E., Tamura, H., Okudera, T., 2001. Arterial fraction of cerebral blood volume in humans measured by positron emission tomography. Ann. Nucl. Med. 15, 111–116.

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